using lidar to map sinkholes in jefferson county, west virginia - … · 2008. 8. 13. · using...

lUsing LiDAR to map sinkholes in Jefferson County, West Jefferson County, West

VirginiaJohn Young, Biology Division*

U.S. Geological SurveyL t S i C tLeetown Science Center

Kearneysville, WV

*Presented by Craig Neidig, USGS WV Geospatial Liaisony g g, p

August 6, 2008

“Geohazards in Transportation in the Appalachian Region”

Project ObjectivesProject Objectives

* In support of USGS study of water pp yavailability and threats to water supply of Leetown Science Center (Kozar et al.)

1. Acquire LiDAR data to allow for modeling of fine scale surface features

2. Generate fine-scale digital elevation models from LiDAR data

3 Use topographic analysis aerial photography 3. Use topographic analysis, aerial photography, and statistical analysis to map sink holes and potential groundwater recharge zones from fine scale surface modelsfine-scale surface models

Wh i LiDAR?What is LiDAR?• LiDAR = Light Detection And Ranging (aka • LiDAR = Light Detection And Ranging (aka

“airborne laser scanning”)• Laser pulses sent from aircraft in dense Laser pulses sent from aircraft in dense

scanning pattern (0.5-2 meters apart)• Return of laser pulse recorded at aircraft• Time to return, speed of light, and altitude of

plane used to compute surface height (z) of each pulse with high accuracyeach pulse with high accuracy

• Ground position of each laser pulse computed using differential GPS (x,y)using differential GPS (x,y)

What is LiDAR?

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LiDAR returns overlaid on aerial photograph of Leetown Science CenterLiDAR returns overlaid on aerial photograph of Leetown Science Center

LiDAR returns: First (top of canopy, roofs), Last (ground surface)

Data Acquisitionq• Acquired LiDAR data by partnering with

USDA-NRCSUSDA NRCS– Conducted accuracy assessment of LiDAR data

acquisition in exchange for access to data• LiDAR data delivered as first return, last

return, and “bare earth” (vegetation and b ildi d)buildings removed)– Bare earth processed data still had substantial

artifacts from vegetationartifacts from vegetation– Needed method to remove remaining vegetation

signal to examine landform under forest canopy

USGS QA/QC Campaign:1. 38 stations established throughout

county in variety of land surface county in variety of land surface types

2. Survey-grade GPS used to collect surface height data

3 GPS f h i ht d t 3. GPS surface heights compared to mean LiDAR return height within 2 meters of GPS points

4. All but 1 checkpoint were within ±p0.15 meter or less z-value (project spec)

Problem: Find method to locate surface sinks, even under forest canopy

Color Aerial Photo, 0.6 meter pixel resolution, 2003

Data: LiDAR, acquired Spring 2005, delivered Fall 2005

Raw (last return) data gridded to 2m surface

Progressive Curvature FilteringProgressive Curvature Filtering

• Evans and Hudak (2007) proposed a method Evans and Hudak (2007) proposed a method for processing LiDAR data to find ground surface in forests of the interior western U.S.

• Method uses an adaptive, iterative filter that pconsiders scale of variation– Fits “thin-plate splines with tension” at multiple

s l s t i l l t d d t i scales to examine local curvature and determine which points to filter out

• Effective at removing vegetationEffective at removing vegetation

Data processing: Progressive Curvature Filter (Evans and Hudak, 2007)

PCF filtered data gridded to 2m surface

Data processing: Landform analysis

Landform shape in 10 m window, red = concave, blue = convex

Data processing: Landform assessment

Find compact bowl features, possible sinkholes

Field Verification

Sinkhole confirmed !

Another view of the sinkhole…

Field validation results94 it d 55 i it d d94 sites mapped, 55 visited on ground

Sink (throat) found:16 4%

Probable sink (no throat):43 6%16.4% 43.6%

Depression:25.5%

Not a sink:14.5%

Other LiDAR applicationsOther LiDAR applications

• Surface geology (bedding etc )Surface geology (bedding, etc.)• Sinkhole susceptibility by geologic

formationformation• Structure / Fault line tracing• Hydrologic flow anomalies• Vegetation height/structureg g• Landslides / slumping• Landscape visualization / 3D modeling• Landscape visualization / 3D modeling

Leetown LiDAR DEM

• The LiDAR DEM was used as the upper l f h layer of the hydrologic flow model.

• LiDAR imagery aided in id ntif in in identifying anomalous areas within the model and helped resolve issues with elevated heads in certain areasin certain areas.

For additional info, contact John Young (jyoung@usgs.gov)

AcknowledgementsAcknowledgements• John Jones, USGS Geography division,

Reston, VA (collaborator)Reston, VA (collaborator)• Bob Glover, USGS Geography division, Reston,

VA (GPS survey)J ff E USFS M Id h • Jeffrey Evans, USFS, Moscow, Idaho (filtering algorithms)

• Kenny Legleiter, (formerly of) USDA-NRCS, Kenny Legleiter, (formerly of) USDA NRCS, Ft. Worth, TX (LiDAR flight contracting)

• Amanda Moore, USDA-NRCS, Morgantown, WV (LiDAR data QA)WV (LiDAR data QA)

• Mark Kozar, USGS-WSC, Charleston, WV(Hydrologic modeling)( y g g)

• Study funded by USGS-LSC

For further information:For further information:

John YoungJohn YoungBiogeographerU S G l i l SU.S. Geological SurveyLeetown Science CenterKearneysville, WV 25430ph: 304-724-4469ph: 304 724 4469email: jyoung@usgs.gov